Ketan leads Strategy & Consulting across
all Accenture Applied Intelligence industry
and functional practices. Ketan partners
with Fortune 500 C-suite executives and
board members to transform their digital
and analytics operating models by applying
intelligence through design-thinking,
AI, data strategy and a cloud-based
He teaches AI at both the Kellogg School of
Management & McCormick School of
Engineering at Northwestern University.
Ketan in based in Chicago, Illinois.
Accenture Applied Intelligence
Robert has over 20 years of experience
shaping and delivering enterprise analytics
strategy and transformation programs for
Fortune 500 clients, including value
targeting, operating model & talent,
analytic delivery models, data &
technology architecture and employee
He has co-authored several white papers on
analytics transformation including “The
Insight-Powered Enterprise” and “Preparing
for a Data Science Transformation”, and
co-created Accenture’s Analytics
Diagnostic (patent pending). Robert is
based in Portland, Oregon.
Accenture Applied Intelligence
Greg leads Communications, Media and
Technology within Accenture Strategy.
His role focuses on helping clients
worldwide achieve high performance
through profitable growth, accelerated
innovation, organizational agility, and
Greg has over 25 years of consulting
experience across the telecom, media,
technology and retail industries, having
focused on new digital business launches,
strategic digital planning, business growth
strategies and cost transformation. Greg is
based in Dallas, Texas.
Senior Managing Director
Athena helps C-suite leaders address
some of their top challenges, including
changing business models, managing
data volumes, addressing analytics
immaturity and transitioning away from
For 20 years, Athena has guided teams in
developing actionable strategies and
plans to create more value through
analytics and define the optimal
technology footprint. A frequent
commentator on digital trends in national
and global media publications, Athena is
based in San Francisco, California.
2AI: BUILT TO SCALE
3. A full 84% of C-suite executives believe they must leverage
Artificial Intelligence (AI) to achieve their growth objectives.
Nearly all C-suite executives view AI as an enabler of their
strategic priorities. And an overwhelming majority believe
achieving a positive return on AI investments requires scaling
across the organization.
Yet 76% acknowledge they struggle when it comes to scaling
it across the business. What’s more, three out of four C-suite
executives believe that if they don’t scale AI in the next five
years, they risk going out of business entirely.
With the stakes higher than ever, what can we learn from
companies that successfully scale AI, achieving nearly 3x the
return on investment and a 30% premium on key financial
3AI: BUILT TO SCALE
4. Nail it, then scale it
To answer that question, Accenture conducted a landmark global study involving
1,500 C-suite executives from organizations across 16 industries.
The study focused on determining the extent to which AI enables the business strategy, the top characteristics required to scale
AI, and the financial results when done successfully. The aim: Help companies progress on their AI journey, from one-off AI
experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth. Three
distinct groups of companies with increasing levels of capability required to successfully scale AI emerged from the research.
In our experience, most companies (80-85%) are
stuck on this path.They conduct AI experiments and
pilots but achieve a low scaling success rate and a
low return on their AI investments. Their efforts tend
to be siloed within a department or team and are
often IT-led. They lack a connection to a business
outcome or strategic imperative. The time and
investment it takes to scale is underestimated,
leaving the full potential of AI untapped.
Only 15-20% of companies have made this leap. These
companies have journeyed beyond proof of concept to
achieve a much higher success rate scaling AI—nearly
double. And a much higher return—nearly three times
their counterparts. As a C-suite priority, these
companies have a clear AI strategy and operating
model linked to the company’s business objectives,
supported by a larger, multi-dimensional team
championed by the Chief AI, Data or Analytics Officer.
However, the scaled AI is generally across point
solutions, e.g., personalization.
Very few (5%) companies have progressed to this point
on their AI journey. These companies have a digital
platform mindset and create a culture of AI with data
and analytics democratized across the organization.
They have scaled thousands of models with a responsible
AI framework. They promote product and service
innovation and realize benefits from increased visibility
into customer and employee expectations. Our
research indicates that industrializing AI will enable
competitive differentiation which is correlated with
significantly higher financial results.
multiple technologies that enable
computers to sense, comprehend, act, and
learn. AI includes techniques such as
machine learning, natural language
processing, knowledge representation,
computational intelligence, among others.
Pilot:Rolling out a capability with real data,
users and processes in a production
environment (using a subset of the relevant
scope). The purpose is to test how the
capability performs with a limited scope and
to make any needed modifications before
expanding to the full applicable scope.
Scale: Extension of the piloted capability
across the full applicable scope with all
relevant data, end users, customers, and
processes. Purpose is to maximize the
application’s value to the organization.
4AI: BUILT TO SCALE
The C-suite executives surveyed
reported positive ROI on their AI
investments. We dug deeper.
Was there any relationship between successfully scaling AI across
the enterprise and key market valuation metrics? What was the
“premium” for being a leader?
Using survey data combined with publicly available financial data,
our team of data scientists created a model to identify the premium
for companies in our sample that successfully scale AI, controlling
for various characteristics of the companies.
We discovered a positive correlation between successfully scaling
AI and three key measures of financial valuation: Enterprise Value/
Revenue Ratio, Price/Earnings Ratio, Price/Sales Ratio.
Companies that were identified as Strategic Scalers realize a success rate of 70% or more in
their AI scaling initiatives and a return on their AI investment of 70% or higher.
5AI: BUILT TO SCALE
US $110m gap
When the 1,500 companies in our research were
analyzed collectively, US$306 billion was spent on AI
applications in the past three years. The ROI gap
amongst them was significant. On average, it
spanned US$110 million between companies stuck in
Proof of Concept and those who have progressed to
becoming Strategic Scalers.1
Overall reported spend on Artificial Intelligence
initiatives over the past three years
The difference in return on AI investments between companies in the Proof of Concept
stage and Strategic Scalers equates to an average of US$110 million.
6AI: BUILT TO SCALE
9. The research revealed three critical
success factors that separate those that
have progressed to Strategically Scaling
and those still in Proof of Concept.
When executives ranked their top
challenges for scaling AI, they placed
“lack of budget” at the bottom of the list.
A possible explanation: AI is a C-suite
priority. So, while it may be challenging to
decide which initiatives to fund first, the
monetary resources, overall, aren’t a problem.
Among the top challenges? The inability to set
up a supportive organizational structure, the
absence of foundational data capabilities, and
the lack of employee adoption. As the study
shows, it’s exactly these aspects where
Strategic Scalers outperform their
counterparts in Proof of Concept.
9AI: BUILT TO SCALE
Creating value from AI requires leaders to anchor
AI in C-suite objectives.
Most global organizations today believe
passionately in the value of data and analytics.
But one life sciences company had been
struggling to move from theory to execution in
implementing data and insights capabilities
across all its business divisions. And they had a
vision to scale a collaborative data-powered
service delivery model to create an internal
marketplace for FAIR (findable, available,
interoperable, reusable) data.
Working with data and digital leadership, the
multi-functional team designed and delivered a
holistic data and analytics strategy while
achieving immediate value through targeted
use-cases in each of the key areas of the business.
In addition to architecting and standing up the
new scalable data and analytics delivery model,
they created a model to make data search easier
and more intuitive and embedded a new data-
driven culture within the organization.
The result? The company’s digital
transformation is speeding ahead, powered by
data analytics insights across its business.
Strategic Scalers pilot and successfully scale more initiatives than their
Proof of Concept counterparts—at a rate of 2:1—and set longer timelines.
They are 65% more likely to report a timeline of one to two years to move
from pilot to scale. And even though they achieve more, Strategic Scalers
spend less. At first glance it may seem paradoxical. But the data indicate
that these leaders are more intentional, with a more realistic expectation
in terms of time to scale—and what it takes to do so responsibly.
To successfully scale, companies need structure and governance in
place. And the Strategic Scalers have both. Nearly three-quarters of
them (71%) say they have a clearly-defined strategy and operating
model for scaling AI in place, while only half of the companies in Proof
of Concept report the same.
Strategic Scalers are also far more likely to have defined processes
and owners with clear accountability and established leadership
support with dedicated AI champions. Initiatives not firmly grounded
in business strategy and lacking a governance construct to oversee
and manage are slower to progress. Turf wars break out over who
“owns” AI and data. And, regardless of the AI platforms used, or the
know-how recruited, misaligned efforts fall flat.
10AI: BUILT TO SCALE
The “smaller” companies in our study
generated revenues between US$1 and
5 billion a year. The largest had revenues of
more than US$30 billion. When it comes to
scaling AI, are there any major differences
between these two groups of companies?
Do the largest companies face lower scaling
success rates due to their organizational
complexity? Or, quite the opposite, do they
achieve higher returns as they untap greater
When we grouped the surveyed companies
by size, we found no significant differences in
scaling success rate or return on AI
investments. So, size is not a factor. It’s all
about instilling the right AI capabilities and
mindset in the organization.
When it comes to scaling AI, Strategic Scalers do the basics brilliantly. Compared to companies in
Proof of Concept, they have a clearly defined strategy and operating model for AI, defined process
and owners for measuring value from AI, clearly defined accountability, appropriate levels of funding,
and flexible business processes with embedded AI. They also scale through reusable assets on
platforms, so successive AI programs are 3-5X faster to market at lower spend.
11AI: BUILT TO SCALE
With a continuously evolving competitor and customer
landscape, a major North American technology company with
global reach needed a dynamic integrated approach for its vast
and complex supply chain. A mix of open source machine
learning tools was used to analyze a range of factors including
demand uncertainty, cost drivers, customer relationships, and rate
of technological change. Personalized supply chain strategies for
four key segments were created with synergies pinpointed to
reduce complexity and maintain economies of scale.
The result? A personalized supply chain transformation with AI at
the core driving new efficiency and effectiveness: 30% faster
speed to delivery with 2X more accurate forecasting. Operating
income up 45% with a reduction in manufacturing and freight
costs of over 25%. Product availability improved over 35%, while
overall supply chain was streamlined with over 90% fewer
Companies from around the world and across industries are using AI to change the fabric of
what they do and how they do it. Strategic Scalers are more likely to achieve a range of
12AI: BUILT TO SCALE
Strategic Scalers recognize the importance of managing data.
Ninety percent of the data in the world was created in just the past 10 years. One-hundred
and seventy-five zettabytes of data will be created by 2025. Yet after years of collecting,
storing, analyzing, and reconfiguring troves of information, most organizations struggle
with the sheer volume of data and how to cleanse, manage, maintain, and consume it.
Strategic Scalers tune out “the noise” surrounding data. They recognize the importance of
business-critical data— identifying financial, marketing, consumer, and master data as
priority domains. And Strategic Scalers are more adept at structuring and managing data.
They have invested heavily in data quality, data management, and data governance
frameworks on the cloud. And they have clear operating models for generation versus
consumption of data. The research shows they are much more likely to wield a larger,
more accurate data set (61% versus 38% of respondents in Proof of Concept). And 67% of
Strategic Scalers integrate both internal and external data sets as a standard practice
compared to 56% of their Proof of Concept counterparts.
What’s more, they use the right AI tools—things like cloud-based data lakes, data
engineering/data science workbenches with model management and governance, data and
analytics marketplaces and search—to manage the data for their applications. From creation
to custodianship to consumption. Strategic Scalers understand the importance of using
more diverse datasets to support initiatives.
13AI: BUILT TO SCALE
Strategic Scalers invest in a data foundation that enables them to scale AI.
Strategic ScalersProof of Concept
With beer sales declining and competitors
drinking up market share on all sides, brewers
are under increasing pressure to find new
growth. One global brewer found a solution
by tapping into new intelligence. The latest
machine learning techniques helped
overcome data veracity issues and develop
more accurate forecasting models, improved
consumer and customer segmentations, and
enhanced sales incrementality.
Their new advanced analytics capabilities are
scaled to over 100+ global datasets, including
sales and forecast data, social media, trade
spend, customer and product master data,
even weather data. They get actionable
data-driven insights in front of their key
business decision makers, from commercial
intelligence to sales and marketing, at
unprecedented speed and scale.
The result: A return of four times the
investment in the first year alone.
14AI: BUILT TO SCALE
The effort of scaling calls for embedding
multi-disciplinary teams throughout the organization
in addition to having sponsorship from the top.
The effort of scaling calls for embedding multi-disciplinary teams
throughout the organization—teams with clear sponsorship from the top
ensuring alignment with the C-suite vision. For Strategic Scalers, these
teams are most often headed by the Chief AI, Data or Analytics Officer.
They’re comprised of data scientists; data modelers; machine learning,
data and AI engineers; visualization experts; data quality, training and
It’s a lesson Strategic Scalers have learned well. In fact, a full 92% of them
leverage multi-disciplinary teams. Embedding them across the organization is
not only a powerful signal about the strategic intent of the scaling effort, it
also enables faster culture and behavior changes. In contrast, those still in
Proof of Concept are more likely to rely on a lone champion within the
technology organization to drive AI efforts.
A leading global convenience store chain with
$60B in revenue and 16,000 locations sought
to gain a more competitive position to stave
off more agile rivals.
They leveraged data science to price their
products more competitively to better match
customer demand across global markets.
Leveraged machine learning and automation
to increase pricing frequency. And
established virtual agents to interact with
their global category management teams to
drive adoption of the new pricing approach.
All of these changes were made possible
thanks to multi-disciplinary teams with skills
in areas like data engineering, visualization,
data quality, and human-centered design.
The initiative is expected to deliver an
expected US$300 million in gross profit
uplift annually once fully scaled.
15AI: BUILT TO SCALE
Companies achieving success scaling AI initiatives are more likely than their Proof
of Concept counterparts to ensure their employees are prepared for the journey:
how AI applies
As companies journey toward maturity in scaling AI, new skill sets emerge as
critical to success. Things like human-centric design and social and behavioral
sciences to drive responsible business practices. The better the blend of skills,
the more sustainable the end result. Those leading the pack ensure employees
have ongoing formal training, an understanding of how AI applies to their role,
and understand and implement responsible AI. Because AI teams are embedded
throughout the organization, and not cordoned off in special projects, the wider
employee base has an opportunity to work side-by-side with them and
appreciate how AI accelerates organizational goals.
Experience is another differentiator when it comes to success. Nearly half
of all Strategic Scalers compared with just a third of respondents in Proof
of Concept report having the necessary experience. The sheer volume
speaks for itself: Strategic Scalers have successfully scaled 114 initiatives
on average, compared with just 53 for those in Proof of Concept.
16AI: BUILT TO SCALE
Industrializing for Growth is a dynamic destination.
It’s important to note that Industrializing for Growth is a dynamic destination that changes as technology evolves.
From our experience, we know of three additional variables that speed companies along their journey to the ultimate
destination: A data-driven culture where AI is driving exponential returns.
Focus on the ‘I’ in ROI
The C-suite views AI investment as the cost of
doing business. They earmark budget for AI
recognizing its criticality for future growth
and spend without the need to prove ROI in
advance to justify the investment.
Adopt a digital platform
mindset to scale
The two main objectives of platforms are
acceleration and extended value. Publishing
data on a platform once for products to
consume through APIs and microservices is
more cost effective. It also drives scale by
breaking down silos and democratizing data
and insights enabling greater collaboration
and innovation across the enterprise and
broader ecosystem of partners.
Build trust through
Responsible AI entails creating a framework
that ensures the ethical, transparent and
accountable use of technologies in a manner
consistent with user expectations,
organizational values and societal laws and
norms. Responsible AI can guard against the
use of biased data or algorithms, ensure that
automated decisions are justified and
explainable, and help maintain user trust and
17AI: BUILT TO SCALE
There are reams of information on the “what”of AI.
But scaling new heights of competitiveness with AI
requires understanding the “how.” And at times
eschewing conventional wisdom that continues to
emerge as AI evolves.
It’s not just about SPEED
It’s about moving deliberately, in the right direction.
It’s not just about MONEY
It’s about aligning your investments to the right places
with the intention of driving large-scale change.
It’s not just about MORE DATA
It’s about investing in your data, deliberately
yet pragmatically, to drive the right insights.
It’s not just about a SINGLE LEADER
It’s about building multi-disciplinary teams that bring
the right capabilities.
18AI: BUILT TO SCALE
19. Scaling the exponential power of AI with digital platforms
across the enterprise is a journey. Those that take on the
lessons of each path will reach a place where business
strategies are seamlessly fused with analytics, leverage a
reusable data foundation, and scale through platforms.
The result: Industrialized growth through unassailable
competitive strength in everything from organizational
effectiveness to brand perception and trust.
Contact the authors to find out more about
how to increase value through scaling AI.
19AI: BUILT TO SCALE
Our research involved 1,500 C-suite
executives from companies with a minimum
revenue of US$1 billion in 12 countries around
the world across 16 industries, with the aim to
uncover the success factors for scaling AI.
Our research, and that of our partners in our
ecosystem, employs ethical and responsible
research methods. Respondents reveal their
identities voluntarily, we anonymize all data
from companies in our data set, and report
results in aggregate. We commit to not using
the data collected to personally identify the
respondents and/or contact the respondents.
Chief Information Officer (CIO) (441)
Chief Financial Officer (CFO) (231)
Chief Operating Officer (COO) (215)
Chief Digital Officer (136)
Chief Innovation Officer (114)
Chief Data and/or Analytics Officer (113)
Chief AI Officer (93)
Chief Strategy Officer (59)
VP/SVP of AI/ Data/Analytics (98)
Banking Capital Markets (100)
Consumer Goods Services (100)
Energy (Oil Gas) (100)
Healthcare (Payers) (100)
High Tech (100)
Industrial Equipment (100)
(Pharma Biotech) (100)
Metals and Mining (100)
Software Platforms (100)
(Hotels Passenger) (100)
United Kingdom (116)
United States (233)
US$10.1 - $30 billion
US$5.1 - $10 billion
US$1 - $5 billion
20AI: BUILT TO SCALE